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Jinxi Xiang, Yonggui Dong, Yunjie Yang
Inverse problems are essential to imaging applications. In this letter, we propose a model-based deep learning network, named FISTA-Net, by combining the merits of interpretability and generality of the model-based Fast Iterative Shrinkage/Thresholding Algorithm (FISTA) and strong regularization and tuning-free advantages of the data-driven neural network. By unfolding the FISTA into a deep network, the architecture of FISTA-Net consists of multiple gradient descent, proximal mapping, and momentum modules in cascade. Different from FISTA, the gradient matrix in FISTA-Net can be updated during iteration and a proximal operator network is developed for nonlinear thresholding which can be learned through end-to-end training. Key parameters of FISTA-Net including the gradient step size, thresholding value and momentum scalar are tuning-free and learned from training data rather than hand-crafted. We further impose positive and monotonous constraints on these parameters to ensure they converge properly. The experimental results, evaluated both visually and quantitatively, show that the FISTA-Net can optimize parameters for different imaging tasks, i.e. Electromagnetic Tomography (EMT) and X-ray Computational Tomography (X-ray CT). It outperforms the state-of-the-art model-based and deep learning methods and exhibits good generalization ability over other competitive learning-based approaches under different noise levels.
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Xiang, J., Dong, Y., & Yang, Y. (2021). FISTA-Net: Learning a Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imaging. IEEE Transactions on Medical Imaging, 40(5), 1329–1339. https://doi.org/10.1109/tmi.2021.3054167
Xiang, Jinxi, et al. "FISTA-Net: Learning a Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imaging." IEEE Transactions on Medical Imaging, vol. 40, no. 5, 2021, pp. 1329–1339, https://doi.org/10.1109/tmi.2021.3054167.
Xiang, Jinxi, Yonggui Dong, and Yunjie Yang. "FISTA-Net: Learning a Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imaging." IEEE Transactions on Medical Imaging 40, no. 5 (2021): 1329–1339. https://doi.org/10.1109/tmi.2021.3054167.
Xiang J., Dong Y., Yang Y. FISTA-Net: Learning a Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imaging // IEEE Transactions on Medical Imaging. — 2021. — Vol. 40, № 5. — P. 1329–1339. — URL: https://doi.org/10.1109/tmi.2021.3054167
@article{xiang2021fistanet,
title = {{FISTA-Net: Learning a Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imaging}},
author = {Jinxi Xiang and Yonggui Dong and Yunjie Yang},
journal = {IEEE Transactions on Medical Imaging},
year = {2021},
volume = {40},
number = {5},
pages = {1329--1339},
doi = {10.1109/tmi.2021.3054167},
url = {https://doi.org/10.1109/tmi.2021.3054167}
}TY - JOUR AU - Xiang, Jinxi AU - Dong, Yonggui AU - Yang, Yunjie TI - FISTA-Net: Learning a Fast Iterative Shrinkage Thresholding Network for Inverse Problems in Imaging JO - IEEE Transactions on Medical Imaging VL - 40 IS - 5 SP - 1329 EP - 1339 PY - 2021 DA - 2021/01/25 DO - 10.1109/tmi.2021.3054167 UR - https://doi.org/10.1109/tmi.2021.3054167 ER -
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